Now that the 2009 H1N1 flu epidemic has passed, it’s possible to
feel some relief. It could have been much worse. Nevertheless, despite
this novel virus strain turning out to be fairly mild relative to
initial fears and prior flu pandemics such as 1918, which was 100
times more lethal, 2009 H1N1 was a major infectious disease outbreak —
12,000 deaths in the United States alone and thousands more worldwide.

Realistically, it’s a question of “when?” not “if” another virus will
emerge as a far more serious public-health threat. And in that
respect, part of the good news from 2009 is that epidemiological
modeling, a powerful tool to assist decision makers, stepped into the
fray. “Our models are a virtual laboratory to ask questions you can’t
ask with real populations,” says Shawn Brown, PSC scientist and
assistant professor in the University
of Pittsburgh Department of
Biostatistics. “We build a population, infect them with the flu, and
then look at mitigation strategies, such as vaccinations or school
closure, and see what effect it has.”

Through the National Institutes of Health
MIDAS (Models of
Infectious Disease Agent Study) Center of Excellence, led by Donald
Burke of the
University of Pittsburgh
Graduate School of Public Health
(GSPH), Brown and collaborators from GSPH and other places used PSC’s
shared memory system, Pople, to model the spread of H1N1 on a regional
basis, both in Allegheny County (which includes Pittsburgh) and in the
Washington, D.C. metropolitan area. They did this modeling during
2009, developing results in real time, in response to requests from
health officials and policy makers, as the severity of the H1N1
outbreak remained in question.

They shared their findings with the Allegheny County Health
Department and officials for the state of Pennsylvania as well as the
U.S.
Biomedical Advanced Research and Development Authority (BARDA),
the U.S. Department of Homeland Security, and the
President’s
Council of Advisors on Science and Technology (PCAST). For three
weeks in the
fall of 2009, Brown and his GSPH colleague Bruce Lee were in effect
embedded with BARDA. “They presented us with scenarios,” says Brown,
“and we did the modeling. Supercomputing really helped. We were able
to get rapid response to complex scenarios. We’re still doing that
today.”

They found, for instance — in a study published in the
Journal of Public Health Management and Practice (December
2009) — that to close schools less than two weeks may slightly
increase infection rates, and that (contrary to Center for Disease
Control recommendations) schools may need to be closed eight weeks or
longer to have a significant impact.

They also used their model to investigate questions about
vaccination priorities. Their findings — Vaccine (May
2010) — support recommendations by the U.S.
Advisory Committee
on Immunization Practices (ACIP) that priority be given to people at
risk for severe complications. Prioritizing at-risk individuals,
rather than only high transmitters (i.e., children), the modeling
showed, may lead to slightly more cases of flu, but it reduces serious
disease and death, and overall economic cost.

Working with Agents

Epidemiological modeling goes back to early 20th-century
mathematical formulations that attempt to quantify the spread of
epidemics by identifying the susceptible proportion of a population
and specifying a rate of transmissibility. As susceptible people
become infected and recover (or not), enough of the population
eventually becomes immune and the epidemic passes. This fairly crude
tool to estimate the length and severity of a disease outbreak has
over the past two decades, with powerful computing and sophisticated
software methods, gained complexity and greatly improved ability to
reflect the reality of how infectious disease spreads.

Increased complexity is especially the case with “agent-based
modeling” (ABM) — a relatively new approach that Brown and his
colleagues used for their 2009 work on H1N1. ABM represents virtual
persons as autonomous “agents” within a synthetic population built
from the most accurate available data (such as the U.S. Census). As
agents become infected with disease, their individual movements within
the population — to work, school, cultural events, etc. — result in
the virus being transmitted to other susceptible agents. Disease
spread is based on algorithms that incorporate randomness, or
stochastic processes.

Spread of H1N1 in the Washington, DC Area
From the epidemiological modeling of Shawn Brown and colleagues, this
graphic shows infected individuals per square mile, coded by color
(increasing from blue to red), at the peak of the 2009 H1N1 epidemic

“Disease spread is a stochastic process,” says Brown. “It’s not
deterministic; you can’t say with certitude when contact between an
infected and susceptible agent will lead to infection. It’s a
statistically based outcome.”

Other forms of epidemiological modeling, which include
compartmental modeling and
network modeling, are less detailed than
ABM, and approximate certain aspects of a population and their
interactions — making it possible to model larger populations and
geographic regions, such as an entire nation. “All these models are
valid,” says Brown, “and all of them are useful. It just depends on
what type of question you want to answer.”

The Pittsburgh MIDAS group’s ABM modeling incorporates disease data
(how long infections last and recovery time), surveillance data (best
available information on how many people are getting sick in real
time) plus social and behavioral data. Families are assigned to
households, children to schools, and agents to workplaces with
commuting distance, location of hospitals and other demographic
factors — developed from census data.

“We need large shared memory, and we’re excited about the UV system at PSC.”

Because ABM represents an entire population inside the computer,
it requires large amounts of memory. For the DC metropolitan area,
MIDAS’s ABM included 7.4 million people, requiring seven gigabytes of
memory. “This is a shared-memory problem,” notes Brown, referring to
massively parallel systems, such as PSC’s Pople, that allow each
processor to access all the memory without message passing.

School Closures & Vaccine Priorities

It might seem obvious that to close schools would help to contain a
flu outbreak — since children in contact with other children, who then
bring it home to their families, is one of the primary ways that flu
spreads through a community. Still, it’s a step that imposes burdens
on parents and, over time, economic costs on a community, as workers
must either stay away from jobs or provide childcare. So, if you close
schools to mitigate the spread of H1N1 (or some other flu), how long —
in order to have optimum impact — should they remain closed?

This question arose during consultations with health officials of
Allegheny County in the fall of 2009, and the MIDAS team addressed it
with their ABM model. Their detailed simulations produced the
unexpected finding that closing schools less than two weeks may
actually prolong an epidemic. Short-duration school closures, they
found, can increase transmission by returning susceptible students
back to school in the middle of an epidemic when they are most
vulnerable to infection.

“Although closing schools may seem like a reasonable way to slow the
spread of flu,” says Lee, “we found it was not effective unless
sustained for at least eight weeks. Closing schools quickly at the
start of an outbreak was much less important than keeping them closed
continually throughout the epidemic.”

The study also found that identifying sick students individually and
holding them away from school had minimal impact. And they found no
significant differences in mitigating an epidemic between individual
school closures and system-wide closure.

Later in 2009, spurred on by the initial limited availability of H1N1
vaccine, Brown and Lee, in collaboration with officials at PCAST and
elsewhere, mounted a series of simulations looking in close detail at
vaccine prioritization. With limited amounts of vaccine, what groups
of people — children, elderly, caregivers, etc. — should be vaccinated
first?

“With the agent-based model,” says Brown, “we could explore this
problem in a much more sophisticated way than with other models.”
Their ABM model of the Washington, DC metro area — which included, for
instance, data on vaccine efficacy and how it varies by age groups —
allowed the researchers to vary parameters and to look at many
prioritization options. Recommendations of ACIP (which advises the
U.S. government on immunization strategy) to prioritize groups at risk
for severe reaction and hospitalization — mainly the elderly, pregnant
women and families with newborns — along with the “high mixer”
population of school-age children were borne out by the modeling.

The Pittsburgh MIDAS team also studied the effects of workplace
vaccination, finding — as might be expected — that prioritizing
workplaces with many employees higher than smaller job-sites reduces
overall economic cost. Attention to economic cost with their ABM
modeling also highlighted the importance of early vaccination; other
research shows an associated cost of $100 for each incidence of flu
averted by early vaccination, which compares with the MIDAS team’s
finding of $21,000 for each incidence averted by school closure.

Brown and the MIDAS team are now working on scaling up their ABM model
to cover the entire United States, incorporating a population of
300-million agents and requiring from 74 to 300 gigabytes of
memory. “As we go to U.S. models and global models,” says Brown, “this
is very much a capacity application. We need large shared memory, and
we’re excited about the UV system at PSC.”